Phylogenetic analysis based on identity and hive plot for species distribution

Phylogenetic analysis based on identity

Load generic libraries

source('configuration.r')

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:gridExtra':
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##     combine

## The following objects are masked from 'package:stats':
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##     filter, lag

## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union

## 
## Attaching package: 'reshape2'

## The following object is masked from 'package:tidyr':
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##     smiths

Load plot specific libraries

library(ape)
library(ggtree)

## ggtree v1.13.0.001  For help: https://guangchuangyu.github.io/software/ggtree
## 
## If you use ggtree in published research, please cite:
## Guangchuang Yu, David Smith, Huachen Zhu, Yi Guan, Tommy Tsan-Yuk Lam. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution 2017, 8(1):28-36, doi:10.1111/2041-210X.12628

## 
## Attaching package: 'ggtree'

## The following object is masked from 'package:ape':
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##     rotate

## The following object is masked from 'package:tidyr':
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##     expand

library(HiveR)

## Warning in rgl.init(initValue, onlyNULL): RGL: unable to open X11 display

## Warning: 'rgl_init' failed, running with rgl.useNULL = TRUE

## Warning in fun(libname, pkgname): couldn't connect to display ":0"

Merge data

df <- read.table('../tables/species_in_assembly_qc_pass.dat', head=TRUE, stringsAsFactors = FALSE, comment.char = '!')
meta.illumina <- read.table('../metadata/illumina_metadata.txt', head=TRUE)[,-2]
meta.nanopore <- read.table('../metadata/nanopore.metadata.txt', head=TRUE, sep='\t', strip.white = TRUE)
meta.merged <- merge(meta.nanopore, meta.illumina,
                     by.x='Illumina_Library_ID',
                     by.y='Library')
meta.merged <- merge(df, meta.merged,
                     by.x='lib',
                     by.y='Nanopore_ID')[,c(4,1:3,5:18)]
colnames(meta.merged)[3] <- 'Species'
## write.table(meta.merged, 'merged_metadata.tsv', quote=F, row.names = F, col.names = T, sep='\t')

## meta.merged <- filter(meta.merged, Antibiotics!='BHI')

Function to plot the tree with heatmap

## function to plot tree
plot.tree <- function(x, color, offset=0.01){
  dist.dat <- read.table(x)
  idx <- str_detect(rownames(dist.dat), 's_') & rownames(dist.dat) %in% meta.merged[,1]
  dist.dat <- dist.dat[idx, idx]
  
  ## clustering
  cluster.full <- hclust(as.dist(dist.dat), method='ward.D')
  clusters <- cutree(cluster.full, h=0.001)
  
  strains <- sapply(unique(clusters), function(x) clusters[clusters==x][1])
  cluster.strains <- hclust(as.dist(dist.dat[names(strains), names(strains)]), method='single')
  
  merged <- merge(data.frame(clusters), meta.merged, by.x=0, by.y=1) %>%  
    mutate(strain=paste0('s', clusters))
  
  antibiotics <- select(merged, clusters, Antibiotics) %>% group_by(clusters, Antibiotics) %>% 
    count() %>% spread(Antibiotics,n,fill=0) %>% merge(data.frame(strains, id=names(strains)), by.x=1, by.y=1) %>% 
    select(-clusters, -BHI) %>% data.frame(row.names = 'id')
  antibiotics[antibiotics > 0] <- 'D'
  
  p <- ggtree(as.phylo(cluster.strains), layout="fan", open.angle=45, lwd=1.5) %<+%
    merged + 
    geom_tippoint(size=3, shape=19, col=color) +
    geom_tiplab2(aes(label=strain), size=5, offset=0.0015)
  
  p <- gheatmap(p, offset=offset, antibiotics, color='black', colnames_offset_y = 0.3,##colnames_offset_x=10,
                colnames_angle = 60, hjust =1, font.size=6.5) + scale_fill_manual(values=c('white', color)) + 
    theme(legend.position ='none')
  return(list(p=p, dat=merged))
}

Generate tree plots

g1 <- plot.tree('../tables/Acinetobacter_baumannii_mummer_heatmap.dat', 'red', 0.007)

## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.

g2 <- plot.tree('../tables/Enterococcus_faecalis_mummer_heatmap.dat', 'blue', 0.004)

## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.

g3 <- plot.tree('../tables/Staphylococcus_aureus_mummer_heatmap.dat', 'darkgreen', 0.005)

## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.

g4 <- plot.tree('../tables/Elizabethkingia_anophelis_mummer_heatmap.dat', 'gold', 0.005)

## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.

g5 <- plot.tree('../tables/Staphylococcus_epidermidis_mummer_heatmap.dat', 'orange', 0.004)

## Scale for 'fill' is already present. Adding another scale for 'fill',
## which will replace the existing scale.

Acinetobacter baumannii

g1$p

Enterococcus faecalis

g2$p

Staphylococcus aureus

g3$p

Elizabethkingia anophelis

g4$p

Staphylococcus epidermidis

g5$p

Save plots

## png 
##   2

Hive plot for species distribution

Generate edge data

rbind(g1$dat, g2$dat, g3$dat, g4$dat, g5$dat) %>%
  group_by(Species, clusters, Sample_type, Room_type, #bed_number,
           timept, Sample_ID.y, Cubicle_room) %>%
  tally()  %>% ungroup() %>% 
  mutate(clusters=sprintf("%02d", clusters)) %>% 
  group_by(Species, clusters, Sample_type, Cubicle_room, Room_type, timept) %>% 
  count %>% select(-nn) %>% 
  mutate(label1='Strain', label2='Site', label3='Room') %>% 
  unite(n1,c(label1, Species, clusters), sep='=', remove=F) %>% 
  unite(n2,c(label2, Sample_type), sep='=', remove=F) %>% 
  unite(n3,c(label3, Room_type, Cubicle_room), sep='=',remove=F) %>% 
  select(-label1, -label2, -label3) -> edge_data

## remove strains only occurred once at one place
edge_data <- filter(edge_data, n1%in% 
         (edge_data %>% group_by(n1) %>% count %>% filter(n>1))$n1)

Hive plot function

hiveplot <- function(species, col){ 
  edge_plot <- filter(edge_data ,Species==species)
  strain_col <- col
  
  rbind(data.frame(x1=edge_plot$n1, x2=edge_plot$n2, color=edge_plot$timept) ,
             data.frame(x1=edge_plot$n1, x2=edge_plot$n3, color=edge_plot$timept)
             ) %>% 
    group_by(x1, x2, color) %>% count() %>% 
    select(x1, x2, weight=n, color) %>% 
    arrange(desc(x1,x2)) -> e
  
  hive <- edge2HPD(data.frame(e[,1:3]))
  hive$nodes$axis <- as.integer(as.factor(str_split_fixed(hive$nodes$lab, '=', 2)[,1]))
  hive$nodes$tag <- str_split_fixed(hive$nodes$lab, '=', 3)[,1] 
  ## species color
  hive$nodes$color[ hive$nodes$tag == 'Strain'] <- strain_col
  ## site color
  colors <- sapply(c(pal_simpsons('springfield')(16)), adjustcolor, alpha.f=0.9)
  colormap <- data.frame(site=levels(edge_data$Sample_type), col=colors[1:9], row.names = 1, stringsAsFactors = F)
  site.id <- hive$nodes$tag=='Site'
  hive$nodes$color[site.id] <- colormap[str_split_fixed(hive$nodes$lab, '=', 2)[site.id, 2], ]
  ## room color
  colormap <-data.frame(room=unique(edge_data$Room_type), col=colors[c(13,15,16)], row.names = 1, stringsAsFactors = F)
  room.id <- hive$nodes$tag=='Room'
  hive$nodes$color[room.id] <- colormap[str_split_fixed(hive$nodes$lab[room.id], '=', 3)[,2], ]
  
  hive$nodes$size=2
  hive$edges$weight <- hive$edges$weight*3-1
  hive$edges$color <- ifelse(e$color<2, '#ff990055','#66ccff55')
  #hive$edges$color <- ifelse(e$color<2,'#66ccff77',  '#ff330077')
  tmp <- data.frame(node.lab=hive$nodes$lab, node.text=hive$nodes$lab, angle=0, radius=0, offset=-0.5, hjust=1, vjust=0.5)
  mutate(tmp, node.text=str_replace(node.text, 'Strain=.*=', 'Strain=s')) %>% 
    separate(col=node.text, into=c('lab','node.text'), '=', extra='merge') %>% 
    mutate(node.text=str_replace_all(node.text, '_', ' ')) %>% 
    mutate(node.text=str_replace_all(node.text, '=', ': ')) %>% 
    mutate(offset=ifelse(lab=='Room' , -0.05, -0.04)) %>% 
    select(-lab) %>% 
  write.table('tmp_hive.txt', sep=',', quote=T, row.names = F, col.names = T)
  
  plotHive(hive, ch=0.2, method='ranknorm', bkgnd='white', anNodes = 'tmp_hive.txt')
}

Acinetobacter baumannii

hiveplot('Acinetobacter_baumannii', 'red')

## Warning in brewer.pal(length(unique(HPD$nodes$axis)), "Set1"): minimal value for n is 3, returning requested palette with 3 different levels

Enterococcus faecalis

hiveplot('Enterococcus_faecalis', 'blue')

## Warning in brewer.pal(length(unique(HPD$nodes$axis)), "Set1"): minimal value for n is 3, returning requested palette with 3 different levels

Staphylococcus aureus

hiveplot('Staphylococcus_aureus', 'darkgreen')

## Warning in brewer.pal(length(unique(HPD$nodes$axis)), "Set1"): minimal value for n is 3, returning requested palette with 3 different levels

Elizabethkingia anophelis

hiveplot('Elizabethkingia_anophelis', 'gold')

## Warning in brewer.pal(length(unique(HPD$nodes$axis)), "Set1"): minimal value for n is 3, returning requested palette with 3 different levels

Staphylococcus epidermidis

hiveplot('Staphylococcus_epidermidis', 'orange')

## Warning in brewer.pal(length(unique(HPD$nodes$axis)), "Set1"): minimal value for n is 3, returning requested palette with 3 different levels

Save plot

## Warning in brewer.pal(length(unique(HPD$nodes$axis)), "Set1"): minimal value for n is 3, returning requested palette with 3 different levels

## Warning in brewer.pal(length(unique(HPD$nodes$axis)), "Set1"): minimal value for n is 3, returning requested palette with 3 different levels

## Warning in brewer.pal(length(unique(HPD$nodes$axis)), "Set1"): minimal value for n is 3, returning requested palette with 3 different levels

## Warning in brewer.pal(length(unique(HPD$nodes$axis)), "Set1"): minimal value for n is 3, returning requested palette with 3 different levels

## Warning in brewer.pal(length(unique(HPD$nodes$axis)), "Set1"): minimal value for n is 3, returning requested palette with 3 different levels

## png 
##   2